{"product_id":"small-area-estimation-9781118735787","title":"Small Area Estimation","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003ePraise for the \u003ci\u003eFirst Edition\u003c\/i\u003e \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThis pioneering work, in which Rao provides a comprehensive and up-to-date treatment of small area estimation, will become a classic...I believe that it has the potential to turn small area estimation...into a larger area of importance to both researchers and practitioners.\u003cbr\u003e\u003cb\u003e\u003ci\u003eJournal of the American Statistical Association\u003c\/i\u003e\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWritten by two experts in the field, \u003ci\u003eSmall Area Estimation, Second Edition\u003c\/i\u003e provides a comprehensive and up-to-date account of the methods and theory of small area estimation (SAE), particularly indirect estimation based on explicit small area linking models. The model-based approach to small area estimation offers several advantages including increased precision, the derivation of optimal estimates and associated measures of variability under an assumed model, and the validation of models from the sample data.\u003c\/p\u003e \u003cp\u003eEmphasizing real data throughout, the \u003ci\u003eSecond Edition\u003c\/i\u003e maintai\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\"The book is an excellent reference for practicing statisticians and survey methodologists as well as practitioners interested in learning SAE methods. The \u003ci\u003esecond edition\u003c\/i\u003e is also an ideal textbook for graduate-level courses in SAE and reliable small area statistics.\" (\u003ci\u003eZentralblatt MATH\u003c\/i\u003e, 2016)\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eList of Figures xv\u003c\/p\u003e \u003cp\u003eList of Tables xvii\u003c\/p\u003e \u003cp\u003eForeword to the First Edition xix\u003c\/p\u003e \u003cp\u003ePreface to the Second Edition xxiii\u003c\/p\u003e \u003cp\u003ePreface to the First Edition xxvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 *Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 What is a Small Area? 1\u003c\/p\u003e \u003cp\u003e1.2 Demand for Small Area Statistics 3\u003c\/p\u003e \u003cp\u003e1.3 Traditional Indirect Estimators 4\u003c\/p\u003e \u003cp\u003e1.4 Small Area Models 4\u003c\/p\u003e \u003cp\u003e1.5 Model-Based Estimation 5\u003c\/p\u003e \u003cp\u003e1.6 Some Examples 6\u003c\/p\u003e \u003cp\u003e1.6.1 Health 6\u003c\/p\u003e \u003cp\u003e1.6.2 Agriculture 7\u003c\/p\u003e \u003cp\u003e1.6.3 Income for Small Places 8\u003c\/p\u003e \u003cp\u003e1.6.4 Poverty Counts 8\u003c\/p\u003e \u003cp\u003e1.6.5 Median Income of Four-Person Families 8\u003c\/p\u003e \u003cp\u003e1.6.6 Poverty Mapping 8\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Direct Domain Estimation 9\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 9\u003c\/p\u003e \u003cp\u003e2.2 Design-Based Approach 10\u003c\/p\u003e \u003cp\u003e2.3 Estimation of Totals 11\u003c\/p\u003e \u003cp\u003e2.3.1 Design-Unbiased Estimator 11\u003c\/p\u003e \u003cp\u003e2.3.2 Generalized Regression Estimator 13\u003c\/p\u003e \u003cp\u003e2.4 Domain Estimation 16\u003c\/p\u003e \u003cp\u003e2.4.1 Case of No Auxiliary Information 16\u003c\/p\u003e \u003cp\u003e2.4.2 GREG Domain Estimation 17\u003c\/p\u003e \u003cp\u003e2.4.3 Domain-Specific Auxiliary Information 18\u003c\/p\u003e \u003cp\u003e2.5 Modified GREG Estimator 21\u003c\/p\u003e \u003cp\u003e2.6 Design Issues 23\u003c\/p\u003e \u003cp\u003e2.6.1 Minimization of Clustering 24\u003c\/p\u003e \u003cp\u003e2.6.2 Stratification 24\u003c\/p\u003e \u003cp\u003e2.6.3 Sample Allocation 24\u003c\/p\u003e \u003cp\u003e2.6.4 Integration of Surveys 25\u003c\/p\u003e \u003cp\u003e2.6.5 Dual-Frame Surveys 25\u003c\/p\u003e \u003cp\u003e2.6.6 Repeated Surveys 26\u003c\/p\u003e \u003cp\u003e2.7 *Optimal Sample Allocation for Planned Domains 26\u003c\/p\u003e \u003cp\u003e2.7.1 Case (i) 26\u003c\/p\u003e \u003cp\u003e2.7.2 Case (ii) 29\u003c\/p\u003e \u003cp\u003e2.7.3 Two-Way Stratification: Balanced Sampling 31\u003c\/p\u003e \u003cp\u003e2.8 Proofs 32\u003c\/p\u003e \u003cp\u003e2.8.1 Proof of \u003ci\u003eŶ\u003c\/i\u003e\u003csub\u003eGR\u003c\/sub\u003e(\u003cb\u003e𝐱\u003c\/b\u003e) = \u003cb\u003e𝐗\u003c\/b\u003e 32\u003c\/p\u003e \u003cp\u003e2.8.2 Derivation of Calibration Weights \u003ci\u003e𝑤\u003c\/i\u003e\u003csup\u003e∗\u003c\/sup\u003e\u003ci\u003e\u003csub\u003ej\u003c\/sub\u003e \u003c\/i\u003e32\u003c\/p\u003e \u003cp\u003e2.8.3 Proof of \u003ci\u003eY \u003c\/i\u003e= \u003cb\u003eX^\u003c\/b\u003e\u003ci\u003e\u003csup\u003eT\u003c\/sup\u003e\u003c\/i\u003e\u003cb\u003e𝐁\u003c\/b\u003e\u003cb\u003e\u003ci\u003e^\u003c\/i\u003e\u003c\/b\u003ewhen \u003ci\u003ec\u003csub\u003ej\u003c\/sub\u003e \u003c\/i\u003e= \u003cb\u003e\u003ci\u003e𝝂\u003c\/i\u003e\u003c\/b\u003e\u003ci\u003e\u003csup\u003eT\u003c\/sup\u003e\u003c\/i\u003e\u003cb\u003e𝐗\u003c\/b\u003e\u003ci\u003e\u003csub\u003ej\u003c\/sub\u003e\u003c\/i\u003e 32\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Indirect Domain Estimation 35\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 35\u003c\/p\u003e \u003cp\u003e3.2 Synthetic Estimation 36\u003c\/p\u003e \u003cp\u003e3.2.1 No Auxiliary Information 36\u003c\/p\u003e \u003cp\u003e3.2.2 *Area Level Auxiliary Information 36\u003c\/p\u003e \u003cp\u003e3.2.3 *Unit Level Auxiliary Information 37\u003c\/p\u003e \u003cp\u003e3.2.4 Regression-Adjusted Synthetic Estimator 42\u003c\/p\u003e \u003cp\u003e3.2.5 Estimation of MSE 43\u003c\/p\u003e \u003cp\u003e3.2.6 Structure Preserving Estimation 45\u003c\/p\u003e \u003cp\u003e3.2.7 *Generalized SPREE 49\u003c\/p\u003e \u003cp\u003e3.2.8 *Weight-Sharing Methods 53\u003c\/p\u003e \u003cp\u003e3.3 Composite Estimation 57\u003c\/p\u003e \u003cp\u003e3.3.1 Optimal Estimator 57\u003c\/p\u003e \u003cp\u003e3.3.2 Sample-Size-Dependent Estimators 59\u003c\/p\u003e \u003cp\u003e3.4 James–Stein Method 63\u003c\/p\u003e \u003cp\u003e3.4.1 Common Weight 63\u003c\/p\u003e \u003cp\u003e3.4.2 Equal Variances \u003ci\u003e𝜓\u003csub\u003ei\u003c\/sub\u003e \u003c\/i\u003e= \u003ci\u003e𝜓\u003c\/i\u003e 64\u003c\/p\u003e \u003cp\u003e3.4.3 Estimation of Component MSE 68\u003c\/p\u003e \u003cp\u003e3.4.4 Unequal Variances \u003ci\u003e𝜓\u003csub\u003ei\u003c\/sub\u003e\u003c\/i\u003e 70\u003c\/p\u003e \u003cp\u003e3.4.5 Extensions 71\u003c\/p\u003e \u003cp\u003e3.5 Proofs 71\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Small Area Models 75\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 75\u003c\/p\u003e \u003cp\u003e4.2 Basic Area Level Model 76\u003c\/p\u003e \u003cp\u003e4.3 Basic Unit Level Model 78\u003c\/p\u003e \u003cp\u003e4.4 Extensions: Area Level Models 81\u003c\/p\u003e \u003cp\u003e4.4.1 Multivariate Fay–Herriot Model 81\u003c\/p\u003e \u003cp\u003e4.4.2 Model with Correlated Sampling Errors 82\u003c\/p\u003e \u003cp\u003e4.4.3 Time Series and Cross-Sectional Models 83\u003c\/p\u003e \u003cp\u003e4.4.4 *Spatial Models 86\u003c\/p\u003e \u003cp\u003e4.4.5 Two-Fold Subarea Level Models 88\u003c\/p\u003e \u003cp\u003e4.5 Extensions: Unit Level Models 88\u003c\/p\u003e \u003cp\u003e4.5.1 Multivariate Nested Error Regression Model 88\u003c\/p\u003e \u003cp\u003e4.5.2 Two-Fold Nested Error Regression Model 89\u003c\/p\u003e \u003cp\u003e4.5.3 Two-Level Model 90\u003c\/p\u003e \u003cp\u003e4.5.4 General Linear Mixed Model 91\u003c\/p\u003e \u003cp\u003e4.6 Generalized Linear Mixed Models 92\u003c\/p\u003e \u003cp\u003e4.6.1 Logistic Mixed Models 92\u003c\/p\u003e \u003cp\u003e4.6.2 *Models for Multinomial Counts 93\u003c\/p\u003e \u003cp\u003e4.6.3 Models for Mortality and Disease Rates 93\u003c\/p\u003e \u003cp\u003e4.6.4 Natural Exponential Family Models 94\u003c\/p\u003e \u003cp\u003e4.6.5 *Semi-parametric Mixed Models 95\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Empirical Best Linear Unbiased Prediction (EBLUP): Theory 97\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 97\u003c\/p\u003e \u003cp\u003e5.2 General Linear Mixed Model 98\u003c\/p\u003e \u003cp\u003e5.2.1 BLUP Estimator 98\u003c\/p\u003e \u003cp\u003e5.2.2 MSE of BLUP 100\u003c\/p\u003e \u003cp\u003e5.2.3 EBLUP Estimator 101\u003c\/p\u003e \u003cp\u003e5.2.4 ML and REML Estimators 102\u003c\/p\u003e \u003cp\u003e5.2.5 MSE of EBLUP 105\u003c\/p\u003e \u003cp\u003e5.2.6 Estimation of MSE of EBLUP 106\u003c\/p\u003e \u003cp\u003e5.3 Block Diagonal Covariance Structure 108\u003c\/p\u003e \u003cp\u003e5.3.1 EBLUP Estimator 108\u003c\/p\u003e \u003cp\u003e5.3.2 Estimation of MSE 109\u003c\/p\u003e \u003cp\u003e5.3.3 Extension to Multidimensional Area Parameters 110\u003c\/p\u003e \u003cp\u003e5.4 *Model Identification and Checking 111\u003c\/p\u003e \u003cp\u003e5.4.1 Variable Selection 111\u003c\/p\u003e \u003cp\u003e5.4.2 Model Diagnostics 114\u003c\/p\u003e \u003cp\u003e5.5 *Software 118\u003c\/p\u003e \u003cp\u003e5.6 Proofs 119\u003c\/p\u003e \u003cp\u003e5.6.1 Derivation of BLUP 119\u003c\/p\u003e \u003cp\u003e5.6.2 Equivalence of BLUP and Best Predictor \u003ci\u003eE\u003c\/i\u003e(\u003cb\u003e𝐦\u003c\/b\u003e\u003ci\u003e\u003csup\u003eT\u003c\/sup\u003e\u003c\/i\u003e\u003cb\u003e𝐯\u003c\/b\u003e|\u003cb\u003e𝐀\u003c\/b\u003e\u003ci\u003e\u003csup\u003eT\u003c\/sup\u003e\u003c\/i\u003e\u003cb\u003e𝐲\u003c\/b\u003e) 120\u003c\/p\u003e \u003cp\u003e5.6.3 Derivation of MSE Decomposition (5.2.29) 121\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Empirical Best Linear Unbiased Prediction (EBLUP): Basic Area Level Model 123\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 EBLUP Estimation 123\u003c\/p\u003e \u003cp\u003e6.1.1 BLUP Estimator 124\u003c\/p\u003e \u003cp\u003e6.1.2 Estimation of \u003ci\u003e𝜎\u003c\/i\u003e\u003csup\u003e2\u003c\/sup\u003e\u003ci\u003e\u003csub\u003e𝑣\u003c\/sub\u003e \u003c\/i\u003e126\u003c\/p\u003e \u003cp\u003e6.1.3 Relative Efficiency of Estimators of \u003ci\u003e𝜎\u003c\/i\u003e\u003csup\u003e2\u003c\/sup\u003e\u003ci\u003e\u003csub\u003e𝑣\u003c\/sub\u003e \u003c\/i\u003e128\u003c\/p\u003e \u003cp\u003e6.1.4 *Applications 129\u003c\/p\u003e \u003cp\u003e6.2 MSE Estimation 136\u003c\/p\u003e \u003cp\u003e6.2.1 Unconditional MSE of EBLUP 136\u003c\/p\u003e \u003cp\u003e6.2.2 MSE for Nonsampled Areas 139\u003c\/p\u003e \u003cp\u003e6.2.3 *MSE Estimation for Small Area Means 140\u003c\/p\u003e \u003cp\u003e6.2.4 *Bootstrap MSE Estimation 141\u003c\/p\u003e \u003cp\u003e6.2.5 *MSE of a Weighted Estimator 143\u003c\/p\u003e \u003cp\u003e6.2.6 Mean Cross Product Error of Two Estimators 144\u003c\/p\u003e \u003cp\u003e6.2.7 *Conditional MSE 144\u003c\/p\u003e \u003cp\u003e6.3 *Robust Estimation in the Presence of Outliers 146\u003c\/p\u003e \u003cp\u003e6.4 *Practical Issues 148\u003c\/p\u003e \u003cp\u003e6.4.1 Unknown Sampling Error Variances 148\u003c\/p\u003e \u003cp\u003e6.4.2 Strictly Positive Estimators of \u003ci\u003e𝜎\u003c\/i\u003e\u003csup\u003e2\u003c\/sup\u003e\u003ci\u003e\u003csub\u003e𝑣\u003c\/sub\u003e \u003c\/i\u003e151\u003c\/p\u003e \u003cp\u003e6.4.3 Preliminary Test Estimation 154\u003c\/p\u003e \u003cp\u003e6.4.4 Covariates Subject to Sampling Errors 156\u003c\/p\u003e \u003cp\u003e6.4.5 Big Data Covariates 159\u003c\/p\u003e \u003cp\u003e6.4.6 Benchmarking Methods 159\u003c\/p\u003e \u003cp\u003e6.4.7 Misspecified Linking Model 165\u003c\/p\u003e \u003cp\u003e6.5 *Software 169\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Basic Unit Level Model 173\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 EBLUP Estimation 173\u003c\/p\u003e \u003cp\u003e7.1.1 BLUP Estimator 174\u003c\/p\u003e \u003cp\u003e7.1.2 Estimation of \u003ci\u003e𝜎\u003c\/i\u003e\u003csup\u003e2\u003c\/sup\u003e\u003ci\u003e\u003csub\u003e𝑣\u003c\/sub\u003e \u003c\/i\u003eand \u003ci\u003e𝜎\u003c\/i\u003e\u003csup\u003e2\u003c\/sup\u003e\u003ci\u003e\u003csub\u003ee\u003c\/sub\u003e \u003c\/i\u003e177\u003c\/p\u003e \u003cp\u003e7.1.3 *Nonnegligible Sampling Fractions 178\u003c\/p\u003e \u003cp\u003e7.2 MSE Estimation 179\u003c\/p\u003e \u003cp\u003e7.2.1 Unconditional MSE of EBLUP 179\u003c\/p\u003e \u003cp\u003e7.2.2 Unconditional MSE Estimators 181\u003c\/p\u003e \u003cp\u003e7.2.3 *MSE Estimation: Nonnegligible Sampling Fractions 182\u003c\/p\u003e \u003cp\u003e7.2.4 *Bootstrap MSE Estimation 183\u003c\/p\u003e \u003cp\u003e7.3 *Applications 186\u003c\/p\u003e \u003cp\u003e7.4 *Outlier Robust EBLUP Estimation 193\u003c\/p\u003e \u003cp\u003e7.4.1 Estimation of Area Means 193\u003c\/p\u003e \u003cp\u003e7.4.2 MSE Estimation 198\u003c\/p\u003e \u003cp\u003e7.4.3 Simulation Results 199\u003c\/p\u003e \u003cp\u003e7.5 *M-Quantile Regression 200\u003c\/p\u003e \u003cp\u003e7.6 *Practical Issues 205\u003c\/p\u003e \u003cp\u003e7.6.1 Unknown Heteroscedastic Error Variances 205\u003c\/p\u003e \u003cp\u003e7.6.2 Pseudo-EBLUP Estimation 206\u003c\/p\u003e \u003cp\u003e7.6.3 Informative Sampling 211\u003c\/p\u003e \u003cp\u003e7.6.4 Measurement Error in Area-Level Covariate 216\u003c\/p\u003e \u003cp\u003e7.6.5 Model Misspecification 218\u003c\/p\u003e \u003cp\u003e7.6.6 Semi-parametric Nested Error Model: EBLUP 220\u003c\/p\u003e \u003cp\u003e7.6.7 Semi-parametric Nested Error Model: REBLUP 224\u003c\/p\u003e \u003cp\u003e7.7 *Software 227\u003c\/p\u003e \u003cp\u003e7.8 *Proofs 231\u003c\/p\u003e \u003cp\u003e7.8.1 Derivation of (7.6.17) 231\u003c\/p\u003e \u003cp\u003e7.8.2 Proof of (7.6.20) 232\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 EBLUP: Extensions 235\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 *Multivariate Fay–Herriot Model 235\u003c\/p\u003e \u003cp\u003e8.2 Correlated Sampling Errors 237\u003c\/p\u003e \u003cp\u003e8.3 Time Series and Cross-Sectional Models 240\u003c\/p\u003e \u003cp\u003e8.3.1 *Rao–Yu Model 240\u003c\/p\u003e \u003cp\u003e8.3.2 State-Space Models 243\u003c\/p\u003e \u003cp\u003e8.4 *Spatial Models 248\u003c\/p\u003e \u003cp\u003e8.5 *Two-Fold Subarea Level Models 251\u003c\/p\u003e \u003cp\u003e8.6 *Multivariate Nested Error Regression Model 253\u003c\/p\u003e \u003cp\u003e8.7 Two-Fold Nested Error Regression Model 254\u003c\/p\u003e \u003cp\u003e8.8 *Two-Level Model 259\u003c\/p\u003e \u003cp\u003e8.9 *Models for Multinomial Counts 261\u003c\/p\u003e \u003cp\u003e8.10 *EBLUP for Vectors of Area Proportions 262\u003c\/p\u003e \u003cp\u003e8.11 *Software 264\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Empirical Bayes (EB) Method 269\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 269\u003c\/p\u003e \u003cp\u003e9.2 Basic Area Level Model 270\u003c\/p\u003e \u003cp\u003e9.2.1 EB Estimator 271\u003c\/p\u003e \u003cp\u003e9.2.2 MSE Estimation 273\u003c\/p\u003e \u003cp\u003e9.2.3 Approximation to Posterior Variance 275\u003c\/p\u003e \u003cp\u003e9.2.4 *EB Confidence Intervals 281\u003c\/p\u003e \u003cp\u003e9.3 Linear Mixed Models 287\u003c\/p\u003e \u003cp\u003e9.3.1 EB Estimation of \u003ci\u003e𝜇\u003csub\u003ei\u003c\/sub\u003e \u003c\/i\u003e= \u003cb\u003e𝐥\u003c\/b\u003e\u003ci\u003e\u003csub\u003ei\u003c\/sub\u003e\u003csup\u003eT\u003c\/sup\u003e\u003c\/i\u003e\u003cb\u003e\u003ci\u003e𝜷 \u003c\/i\u003e\u003c\/b\u003e+ \u003cb\u003e𝐦\u003c\/b\u003e\u003ci\u003e\u003csup\u003eT\u003c\/sup\u003e\u003csub\u003ei\u003c\/sub\u003e \u003c\/i\u003e\u003cb\u003e𝐯\u003c\/b\u003e\u003ci\u003e\u003csub\u003ei\u003c\/sub\u003e\u003c\/i\u003e 287\u003c\/p\u003e \u003cp\u003e9.3.2 MSE Estimation 288\u003c\/p\u003e \u003cp\u003e9.3.3 Approximations to the Posterior Variance 288\u003c\/p\u003e \u003cp\u003e9.4 *EB Estimation of General Finite Population Parameters 289\u003c\/p\u003e \u003cp\u003e9.4.1 BP Estimator Under a Finite Population 290\u003c\/p\u003e \u003cp\u003e9.4.2 EB Estimation Under the Basic Unit Level Model 290\u003c\/p\u003e \u003cp\u003e9.4.3 FGT Poverty Measures 293\u003c\/p\u003e \u003cp\u003e9.4.4 Parametric Bootstrap for MSE Estimation 294\u003c\/p\u003e \u003cp\u003e9.4.5 ELL Estimation 295\u003c\/p\u003e \u003cp\u003e9.4.6 Simulation Experiments 296\u003c\/p\u003e \u003cp\u003e9.5 Binary Data 298\u003c\/p\u003e \u003cp\u003e9.5.1 *Case of No Covariates 299\u003c\/p\u003e \u003cp\u003e9.5.2 Models with Covariates 304\u003c\/p\u003e \u003cp\u003e9.6 Disease Mapping 308\u003c\/p\u003e \u003cp\u003e9.6.1 Poisson–Gamma Model 309\u003c\/p\u003e \u003cp\u003e9.6.2 Log-Normal Models 310\u003c\/p\u003e \u003cp\u003e9.6.3 Extensions 312\u003c\/p\u003e \u003cp\u003e9.7 *Design-Weighted EB Estimation: Exponential Family Models 313\u003c\/p\u003e \u003cp\u003e9.8 Triple-Goal Estimation 315\u003c\/p\u003e \u003cp\u003e9.8.1 Constrained EB 316\u003c\/p\u003e \u003cp\u003e9.8.2 Histogram 318\u003c\/p\u003e \u003cp\u003e9.8.3 Ranks 318\u003c\/p\u003e \u003cp\u003e9.9 Empirical Linear Bayes 319\u003c\/p\u003e \u003cp\u003e9.9.1 LB Estimation 319\u003c\/p\u003e \u003cp\u003e9.9.2 Posterior Linearity 322\u003c\/p\u003e \u003cp\u003e9.10 Constrained LB 324\u003c\/p\u003e \u003cp\u003e9.11 *Software 325\u003c\/p\u003e \u003cp\u003e9.12 Proofs 330\u003c\/p\u003e \u003cp\u003e9.12.1 Proof of (9.2.11) 330\u003c\/p\u003e \u003cp\u003e9.12.2 Proof of (9.2.30) 330\u003c\/p\u003e \u003cp\u003e9.12.3 Proof of (9.8.6) 331\u003c\/p\u003e \u003cp\u003e9.12.4 Proof of (9.9.1) 331\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Hierarchical Bayes (HB) Method 333\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 333\u003c\/p\u003e \u003cp\u003e10.2 MCMC Methods 335\u003c\/p\u003e \u003cp\u003e10.2.1 Markov Chain 335\u003c\/p\u003e \u003cp\u003e10.2.2 Gibbs Sampler 336\u003c\/p\u003e \u003cp\u003e10.2.3 M–H Within Gibbs 336\u003c\/p\u003e \u003cp\u003e10.2.4 Posterior Quantities 337\u003c\/p\u003e \u003cp\u003e10.2.5 Practical Issues 339\u003c\/p\u003e \u003cp\u003e10.2.6 Model Determination 342\u003c\/p\u003e \u003cp\u003e10.3 Basic Area Level Model 347\u003c\/p\u003e \u003cp\u003e10.3.1 Known \u003ci\u003e𝜎\u003c\/i\u003e\u003csup\u003e2\u003c\/sup\u003e\u003ci\u003e\u003csub\u003e𝑣\u003c\/sub\u003e \u003c\/i\u003e347\u003c\/p\u003e \u003cp\u003e10.3.2 *Unknown \u003ci\u003e𝜎\u003c\/i\u003e\u003csup\u003e2\u003c\/sup\u003e\u003ci\u003e\u003csub\u003e𝑣\u003c\/sub\u003e\u003c\/i\u003e: Numerical Integration 348\u003c\/p\u003e \u003cp\u003e10.3.3 Unknown \u003ci\u003e𝜎\u003c\/i\u003e\u003csup\u003e2\u003c\/sup\u003e\u003ci\u003e\u003csub\u003e𝑣\u003c\/sub\u003e\u003c\/i\u003e: Gibbs Sampling 351\u003c\/p\u003e \u003cp\u003e10.3.4 *Unknown Sampling Variances \u003ci\u003e𝜓\u003csub\u003ei\u003c\/sub\u003e\u003c\/i\u003e 354\u003c\/p\u003e \u003cp\u003e10.3.5 *Spatial Model 355\u003c\/p\u003e \u003cp\u003e10.4 *Unmatched Sampling and Linking Area Level Models 356\u003c\/p\u003e \u003cp\u003e10.5 Basic Unit Level Model 362\u003c\/p\u003e \u003cp\u003e10.5.1 Known \u003ci\u003e𝜎\u003c\/i\u003e\u003csup\u003e2\u003c\/sup\u003e\u003ci\u003e\u003csub\u003e𝑣\u003c\/sub\u003e \u003c\/i\u003eand \u003ci\u003e𝜎\u003c\/i\u003e\u003csup\u003e2\u003c\/sup\u003e\u003ci\u003e\u003csub\u003ee\u003c\/sub\u003e\u003c\/i\u003e 362\u003c\/p\u003e \u003cp\u003e10.5.2 Unknown \u003ci\u003e𝜎\u003c\/i\u003e\u003csup\u003e2\u003c\/sup\u003e\u003ci\u003e\u003csub\u003e𝑣\u003c\/sub\u003e \u003c\/i\u003eand \u003ci\u003e𝜎\u003c\/i\u003e\u003csup\u003e2\u003c\/sup\u003e\u003ci\u003e\u003csub\u003ee\u003c\/sub\u003e\u003c\/i\u003e: Numerical Integration 363\u003c\/p\u003e \u003cp\u003e10.5.3 Unknown \u003ci\u003e𝜎\u003c\/i\u003e\u003csup\u003e2\u003c\/sup\u003e\u003ci\u003e\u003csub\u003e𝑣\u003c\/sub\u003e \u003c\/i\u003eand \u003ci\u003e𝜎\u003c\/i\u003e\u003csup\u003e2\u003c\/sup\u003e\u003ci\u003e\u003csub\u003ee\u003c\/sub\u003e\u003c\/i\u003e: Gibbs Sampling 364\u003c\/p\u003e \u003cp\u003e10.5.4 Pseudo-HB Estimation 365\u003c\/p\u003e \u003cp\u003e10.6 General ANOVA Model 368\u003c\/p\u003e \u003cp\u003e10.7 *HB Estimation of General Finite Population Parameters 369\u003c\/p\u003e \u003cp\u003e10.7.1 HB Estimator under a Finite Population 370\u003c\/p\u003e \u003cp\u003e10.7.2 Reparameterized Basic Unit Level Model 370\u003c\/p\u003e \u003cp\u003e10.7.3 HB Estimator of a General Area Parameter 372\u003c\/p\u003e \u003cp\u003e10.8 Two-Level Models 374\u003c\/p\u003e \u003cp\u003e10.9 Time Series and Cross-Sectional Models 377\u003c\/p\u003e \u003cp\u003e10.10 Multivariate Models 381\u003c\/p\u003e \u003cp\u003e10.10.1 Area Level Model 381\u003c\/p\u003e \u003cp\u003e10.10.2 Unit Level Model 382\u003c\/p\u003e \u003cp\u003e10.11 Disease Mapping Models 383\u003c\/p\u003e \u003cp\u003e10.11.1 Poisson-Gamma Model 383\u003c\/p\u003e \u003cp\u003e10.11.2 Log-Normal Model 384\u003c\/p\u003e \u003cp\u003e10.11.3 Two-Level Models 386\u003c\/p\u003e \u003cp\u003e10.12 *Two-Part Nested Error Model 388\u003c\/p\u003e \u003cp\u003e10.13 Binary Data 389\u003c\/p\u003e \u003cp\u003e10.13.1 Beta-Binomial Model 389\u003c\/p\u003e \u003cp\u003e10.13.2 Logit-Normal Model 390\u003c\/p\u003e \u003cp\u003e10.13.3 Logistic Linear Mixed Models 393\u003c\/p\u003e \u003cp\u003e10.14 *Missing Binary Data 397\u003c\/p\u003e \u003cp\u003e10.15 Natural Exponential Family Models 398\u003c\/p\u003e \u003cp\u003e10.16 Constrained HB 399\u003c\/p\u003e \u003cp\u003e10.17 *Approximate HB Inference and Data Cloning 400\u003c\/p\u003e \u003cp\u003e10.18 Proofs 402\u003c\/p\u003e \u003cp\u003e10.18.1 Proof of (10.2.26) 402\u003c\/p\u003e \u003cp\u003e10.18.2 Proof of (10.2.32) 402\u003c\/p\u003e \u003cp\u003e10.18.3 Proof of (10.3.13)–(10.3.15) 402\u003c\/p\u003e \u003cp\u003eReferences 405\u003c\/p\u003e \u003cp\u003eAuthor Index 431\u003c\/p\u003e \u003cp\u003eSubject Index 437\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default 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